Matlab Functions For Neural Networks Models

Download Matlab Functions For Neural Networks Models full books in PDF, epub, and Kindle. Read online Matlab Functions For Neural Networks Models ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

MATLAB Functions for Neural Networks Models

Author : Smith A.
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781540591203
Total Pages : 470 pages
Book Rating : 4.5/5 (912 download)

DOWNLOAD NOW!

Book Synopsis MATLAB Functions for Neural Networks Models by : Smith A.

Download or read book MATLAB Functions for Neural Networks Models written by Smith A. and published by Createspace Independent Publishing Platform. This book was released on 2016-11-23 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: The MATLAB Neural Network Toolbox provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the next: .Deep learning, including convolutional neural networks and autoencoders .Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) .Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) .Unsupervised learning algorithms, including self-organizing maps and competitive layers .Apps for data-fitting, pattern recognition, and clustering .Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance .Simulink blocks for building and evaluating neural networks and for control systems applications

Neural Networks Using Matlab, Function Approximation and Regression

Author : K. Taylor
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781543008562
Total Pages : pages
Book Rating : 4.0/5 (85 download)

DOWNLOAD NOW!

Book Synopsis Neural Networks Using Matlab, Function Approximation and Regression by : K. Taylor

Download or read book Neural Networks Using Matlab, Function Approximation and Regression written by K. Taylor and published by Createspace Independent Publishing Platform. This book was released on 2017-02-09 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications This book delves into the applications of neural networks to fit functions and regression models

Machine Learning with Neural Networks Using MATLAB

Author : J. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781544170213
Total Pages : 0 pages
Book Rating : 4.1/5 (72 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning with Neural Networks Using MATLAB by : J. Smith

Download or read book Machine Learning with Neural Networks Using MATLAB written by J. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-02-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data. MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, dynamic system modeling and control and most machine learning techniques. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications

PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 : 1716601568
Total Pages : 239 pages
Book Rating : 4.7/5 (166 download)

DOWNLOAD NOW!

Book Synopsis PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB by : Cesar Perez Lopez

Download or read book PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on 2020-09-06 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. Different work fields with neural networks and predictive analytics techniques are listed below: The multilayer perceptron (MLP), A radial basis function (RBF), Support vector machines (SVM), Fit regression models with neural networks, Time series neural networks, Hopfield and linear neural networks, Generalized regression and LVQ neural networks, Adaptative linear filters and non linear problems

Deep Learning Using MATLAB. Neural Network Applications

Author : K. Taylor
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781543144567
Total Pages : 334 pages
Book Rating : 4.1/5 (445 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning Using MATLAB. Neural Network Applications by : K. Taylor

Download or read book Deep Learning Using MATLAB. Neural Network Applications written by K. Taylor and published by Createspace Independent Publishing Platform. This book was released on 2017-02-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Deep learning is part of a broader family of machine learning methods based on learning representations of data. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications This book develops deep learning, including convolutional neural networks and autoencoders and other types of advanced neural networks

MATLAB for Machine Learning

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1788399390
Total Pages : 374 pages
Book Rating : 4.7/5 (883 download)

DOWNLOAD NOW!

Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro

Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Computational Methods for Deep Learning

Author : Wei Qi Yan
Publisher : Springer Nature
ISBN 13 : 3030610810
Total Pages : 134 pages
Book Rating : 4.0/5 (36 download)

DOWNLOAD NOW!

Book Synopsis Computational Methods for Deep Learning by : Wei Qi Yan

Download or read book Computational Methods for Deep Learning written by Wei Qi Yan and published by Springer Nature. This book was released on 2020-12-04 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.

Big Data and Deep Learning. Examples with MATLAB

Author : C. Perez
Publisher : CESAR PEREZ
ISBN 13 : 1716877563
Total Pages : 328 pages
Book Rating : 4.7/5 (168 download)

DOWNLOAD NOW!

Book Synopsis Big Data and Deep Learning. Examples with MATLAB by : C. Perez

Download or read book Big Data and Deep Learning. Examples with MATLAB written by C. Perez and published by CESAR PEREZ. This book was released on 2020-05-31 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Deep learning has been characterized as a buzzword, or a rebranding of neural networks. This book deeps in big data and deep learning techniques

MATLAB Deep Learning

Author : Phil Kim
Publisher : Apress
ISBN 13 : 1484228456
Total Pages : 162 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!

Book Synopsis MATLAB Deep Learning by : Phil Kim

Download or read book MATLAB Deep Learning written by Phil Kim and published by Apress. This book was released on 2017-06-15 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

A Practical Approach for Machine Learning and Deep Learning Algorithms

Author : Abhishek Kumar Pandey
Publisher : BPB Publications
ISBN 13 : 9388511131
Total Pages : 322 pages
Book Rating : 4.3/5 (885 download)

DOWNLOAD NOW!

Book Synopsis A Practical Approach for Machine Learning and Deep Learning Algorithms by : Abhishek Kumar Pandey

Download or read book A Practical Approach for Machine Learning and Deep Learning Algorithms written by Abhishek Kumar Pandey and published by BPB Publications. This book was released on 2019-09-18 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guide covering topics from machine learning, regression models, neural network to tensor flow DESCRIPTION Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it. KEY FEATURES Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data. Machine learning workflow for health monitoring. The neural network domain and implementation in MATLAB with explicit explanation of code and results. How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula. Machine learning workflow for health monitoring. WHAT WILL YOU LEARN Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning WHO THIS BOOK IS FOR The book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement. We have touched all basic algorithms of machine learning in detail with a practical approach. Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time. Table of Contents _1. Ê Ê Pre-requisite to Machine Learning 2. Ê Ê An introduction to Machine Learning 3. Ê Ê Finding Natural Patterns in Data 4. Ê Ê Building Classification Methods 5. Ê Ê Data Pre-Processing in Python 6. Ê Ê Building Regression Models 7. Ê Ê Creating Neural Networks 8. Ê Ê Introduction to Deep Learning

Computational Ecology

Author : Wenjun Zhang
Publisher : World Scientific
ISBN 13 : 9814282634
Total Pages : 310 pages
Book Rating : 4.8/5 (142 download)

DOWNLOAD NOW!

Book Synopsis Computational Ecology by : Wenjun Zhang

Download or read book Computational Ecology written by Wenjun Zhang and published by World Scientific. This book was released on 2010 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ch. 1. Introduction. 1. Computational ecology. 2. Artificial neural networks and ecological applications -- pt. I. Artificial neural networks : principles, theories and algorithms. ch. 2. Feedforward neural networks. 1. Linear separability and perceptron. 2. Some analogies of multilayer feedforward networks. 3. Functionability of multilayer feedforward networks. ch. 3. Linear neural networks. 1. Linear neural networks. 2. LMS rule. ch. 4. Radial basis function neural networks. 1. Theory of RBF neural network. 2. Regularized RBF neural network. 3. RBF neural network learning. 4. Probabilistic neural network. 5. Generalized regression neural network. 6. Functional link neural network. 7. Wavelet neural network. ch. 5. BP neural network. 1. BP algorithm. 2. BP theorem. 3. BP training. 4. Limitations and improvements of BP algorithm. ch. 6. Self-organizing neural networks. 1. Self-organizing feature map neural network. 2. Self-organizing competitive learning neural network. 3. Hamming neural network. 4. WTA neural network. 5. LVQ neural network. 6. Adaptive resonance theory. ch. 7. Feedback neural networks. 1. Elman neural network. 2. Hopfield neural networks. 3. Simulated annealing. 4. Boltzmann machine. ch. 8. Design and customization of artificial neural networks. 1. Mixture of experts. 2. Hierarchical mixture of experts. 3. Neural network controller. 4. Customization of neural networks. ch. 9. Learning theory, architecture choice and interpretability of neural networks. 1. Learning theory. 2. Architecture choice. 3. Interpretability of neural networks. ch. 10. Mathematical foundations of artificial neural networks. 1. Bayesian methods. 2. Randomization, bootstrap and Monte Carlo techniques. 3. Stochastic process and stochastic differential equation. 4. Interpolation. 5. Function approximation. 6. Optimization methods. 7. Manifold and differential geometry. 8. Functional analysis. 9. Algebraic topology. 10. Motion stability. 11. Entropy of a system. 12. Distance or similarity measures. ch. 11. Matlab neural network toolkit. 1. Functions of perceptron. 2. Functions of linear neural networks. 3. Functions of BP neural network. 4. Functions of self-organizing neural networks. 5. Functions of radial basis neural networks. 6. Functions of probabilistic neural network. 7. Function of generalized regression neural network. 8. Functions of Hopfield neural network. 9. Function of Elman neural network -- pt. II. Applications of artificial neural networks in ecology. ch. 12. Dynamic modeling of survival process. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 13. Simulation of plant growth process. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 14. Simulation of food intake dynamics. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 15. Species richness estimation and sampling data documentation. 1. Estimation of plant species richness on grassland. 2. Documentation of sampling data of invertebrates. ch. 16. Modeling arthropod abundance from plant composition of grassland community. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 17. Pattern recognition and classification of ecosystems and functional groups. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 18. Modeling spatial distribution of arthropods. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 19. Risk assessment of species invasion and establishment. 1. Invasion risk assessment based on species assemblages. 2. Determination of abiotic factors influencing species invasion. ch. 20. Prediction of surface ozone. 1. BP prediction of daily total ozone. 2. MLP Prediction of hourly ozone levels. ch. 21. Modeling dispersion and distribution of oxide and nitrate pollutants. 1. Modeling nitrogen dioxide dispersion. 2. Simulation of nitrate distribution in ground water. ch. 22. Modeling terrestrial biomass. 1. Estimation of aboveground grassland biomass. 2. Estimation of trout biomass

Methods and Procedures for the Verification and Validation of Artificial Neural Networks

Author : Brian J. Taylor
Publisher : Springer Science & Business Media
ISBN 13 : 0387294856
Total Pages : 280 pages
Book Rating : 4.3/5 (872 download)

DOWNLOAD NOW!

Book Synopsis Methods and Procedures for the Verification and Validation of Artificial Neural Networks by : Brian J. Taylor

Download or read book Methods and Procedures for the Verification and Validation of Artificial Neural Networks written by Brian J. Taylor and published by Springer Science & Business Media. This book was released on 2006-03-20 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.

Predictive Analytics With Neural Networks Using Matlab

Author : J. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781544169613
Total Pages : 242 pages
Book Rating : 4.1/5 (696 download)

DOWNLOAD NOW!

Book Synopsis Predictive Analytics With Neural Networks Using Matlab by : J. Smith

Download or read book Predictive Analytics With Neural Networks Using Matlab written by J. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-02-27 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions. They can be applied to problems of prediction, classification or control in a wide spectrum of fields such as finance, cognitive psychology/neuroscience, medicine, engineering, and physics. Neural networks are used when the exact nature of the relationship between inputs and output is not known. A key feature of neural networks is that they learn the relationship between inputs and output through training. There are three types of training used by different neural networks: supervised and unsupervised training and reinforcement learning, with supervised being the most common one. Some examples of neural network training techniques are backpropagation, quick propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc. Some unsupervised network architectures are multilayer perceptrons, Kohonen networks, Hopfield networks, etc. Different work fields with neural networks and predictive analytics techniques are developed in this book: -The multilayer perceptron (MLP) -A radial basis function (RBF) i -Fit regression models with neural networks. -Time series neural networks. Modeling and prediction with NARX and time delay networks. -Hopfield and linear neural networks. -Generalized regression and LVQ neural networks. -Adaptative linear filters and non linear problems. Used for linear and nonlinear prediction

Deep Learning Applications, Volume 2

Author : M. Arif Wani
Publisher : Springer
ISBN 13 : 9789811567582
Total Pages : 300 pages
Book Rating : 4.5/5 (675 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning Applications, Volume 2 by : M. Arif Wani

Download or read book Deep Learning Applications, Volume 2 written by M. Arif Wani and published by Springer. This book was released on 2020-12-14 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

MATLAB Programming

Author : Dingyü Xue
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110663708
Total Pages : 308 pages
Book Rating : 4.1/5 (16 download)

DOWNLOAD NOW!

Book Synopsis MATLAB Programming by : Dingyü Xue

Download or read book MATLAB Programming written by Dingyü Xue and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-03-23 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents fundamentals in MATLAB programming, including data and statement structures, control structures, function writing and bugging in MATLAB programming, followed by the presentations of algebraic computation, transcendental function evaluations and data processing. Advanced topics such as MATLAB interfacing, object-oriented programming and graphical user interface design are also addressed.

Machine and Deep Learning Using MATLAB

Author : Kamal I. M. Al-Malah
Publisher : John Wiley & Sons
ISBN 13 : 139420910X
Total Pages : 596 pages
Book Rating : 4.3/5 (942 download)

DOWNLOAD NOW!

Book Synopsis Machine and Deep Learning Using MATLAB by : Kamal I. M. Al-Malah

Download or read book Machine and Deep Learning Using MATLAB written by Kamal I. M. Al-Malah and published by John Wiley & Sons. This book was released on 2023-10-12 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE AND DEEP LEARNING In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues. Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning) Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response) Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps Retraining and creation for image labeling, object identification, regression classification, and text recognition Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.

Neural Networks And Expert Systems In Medicine And Healthcare - Proceedings Of The Third International Conference

Author : Antonina Starita
Publisher : World Scientific
ISBN 13 : 9814544450
Total Pages : 368 pages
Book Rating : 4.8/5 (145 download)

DOWNLOAD NOW!

Book Synopsis Neural Networks And Expert Systems In Medicine And Healthcare - Proceedings Of The Third International Conference by : Antonina Starita

Download or read book Neural Networks And Expert Systems In Medicine And Healthcare - Proceedings Of The Third International Conference written by Antonina Starita and published by World Scientific. This book was released on 1998-08-14 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in medical expert systems, neural networks and other artificial intelligence techniques is on the increase as more healthcare providers realise their potential, and engineers and scientists are discovering that medicine and healthcare are very fertile areas for developing new, or applying existing, intelligent algorithms to real problems. Intelligent systems make it possible to capture expert medical knowledge and to discover new knowledge so as to improve in-patient monitoring, data analysis and decision making, and hence the quality of healthcare.This book contains features which include: neural networks and expert systems techniques, as well as medical neural networks and expert systems. It should be of interest to managers, academics, engineers, scientists and medical practitioners involved in the funding, development and use of intelligent medical systems.